The pattern recognition approach to self-tuning is unique. One particular pattern recognition approach is the performance feedback technique. The performance feedback technique uses direct-performance feedback of a monitored process variable to determine the required action for optimizing the process. More specifically, a pattern-recognition, self-tuning controller continuously monitors and automatically evaluates a closed-loop response pattern of a measured process variable to naturally occurring, unmeasured upsets caused by a change in set point or load. After each upset, closed-loop error response characteristics such as overshoot and decay are identified and compared with desired characteristics. Differences between the desired characteristics and the measured characteristics are then used to automatically generate new tuning values for adjusting the controller according to the requirements of the process in order to achieve an error response target shape.
Adaptive process control systems that automatically identify and evaluate process response characteristics of the closed-loop response system are known in the art. The first successful adaptive control system was invented by Edgar H. Bristol, II and is disclosed in U.S. Pat. No. 3,798,426. According to Bristol's method, the adaptive system identifies both dead times and rise times of the closed loop response. These times are considered to have a significant relation to process characteristics. The Bristol control system scaled the rise time according to preselected scaling constants to establish evaluation intervals. During the evaluation intervals the actual error response is compared to a target error response. The results of this comparison are used to adapt the controller parameters.
The Bristol adaptive system provides effective adaptive control, but requires that the user preselect critical operating parameters such as the scaling constants. The scaling constants are used to normalize the measured pattern features. The normalization is necessary because the adaptive controller adapts control parameters according to the shape of the response as described by the observed pattern features. This makes the adaptor very sensitive to the shape of the error response.
An improved pattern-recognition controller was developed by Thomas W. Kraus, U.S. Pat. No. 4,602,326. The Kraus system monitors peaks of the error response signal. The measured peaks are used to define pattern features of the error response signal that are relevant to both the system response and the characteristics of the process. The controller compares the measured pattern features against a set of target pattern features, and adapts the controller parameters to optimize the control action.
The Kraus' system also has critical parameters that must be specified by an operator. For example, choosing the pre-specified wait period for the peak search. The Kraus system also has a tendency to tighten the tuning of the process until the response becomes excessively oscillatory. This tendency resulted in the controller overcorrecting the process to obtain an overdamped response.
In the above-cited U.S. application Ser. No. 07/553,915, filed Jul. 16, 1990, and herein incorporated by reference, a performance feedback controller is disclosed that responds to natural disturbances. The controller determines selected pattern features of the closed-loop error response that are caused by the detected disturbance. The pattern features are determined from measured peaks of the error response signal. The controller uses these pattern features along with a process type variable and a set of data tables to calculate changes in the control parameters that will achieve rapid convergence to a set of target response signal pattern features.
In the above application, rapid convergence to a target response signal feature is achieved by using an interpolative method for relating the determined pattern features to a set of three desired controller parameters. Therefore, this interpolation method tunes the controller parameters according to performance feedback from a particular detected disturbance. As a result, this method tunes the controller parameters to cause rapid convergence for disturbances similar to the previously detected disturbance. The disclosed controller is, therefore, very sensitive to the types of disturbances applied to the control system. Furthermore, the disclosed controller worked best if the identified pattern features characterize a certain category of response signals, and that these features characterize these signals with sufficiently complete information that the interpolation method can be validly applied. For certain categories of signals, such as nearly pure decaying or expanding sinusoids, the information contained in the pattern features is incomplete. In these cases, the interpolation system cannot be used to determine the necessary parameters of the controller and the controller defaults to a set of expert system rules. These expert system rules do not provide the rapid convergence of the interpolation system, and in fact, often converge very slowly or worse, detune the control system.
A similar difficulty with the interpolation method is that it works best with isolated signals. The controller defines a response signal as isolated if the response signal is detected after an interval of relatively quiet operation. The response signal is isolated for example, if the signal is detected after at least one predefined time interval, defined as a search interval, and the search interval elapsed without any detected signal activity. The signal is non-isolated, for example, when the error signal is a sinusoid superimposed on a ramping linear signal. For non-isolated signals, the controller again defaults to a set of expert system rules that often cause slow system convergence.
To compensate for the interpolation method's difficulty with non-isolated signals, a method for selecting a specific series of peak values is implemented by the controller. This method, described as peak slipping, compares the relative amplitudes of successively measured peaks for the purpose of identifying three successive peaks that provide the sufficient information for the interpolation method used in concert with a system of expert rules. This peak slipping process adds an extra step in the adaption process that requires multiple sets of pattern features to be determined and compared.